Neural community pruning with combinatorial optimization – Google Analysis Weblog

Fashionable neural networks have achieved spectacular efficiency throughout quite a lot of purposes, corresponding to language, mathematical reasoning, and vision. Nonetheless, these networks typically use massive architectures that require plenty of computational assets. This will make it impractical to serve such fashions to customers, particularly in resource-constrained environments like wearables and smartphones. A widely used approach to mitigate the inference prices of pre-trained networks is to prune them by eradicating a few of their weights, in a method that doesn’t considerably have an effect on utility. In commonplace neural networks, every weight defines a connection between two neurons. So after weights are pruned, the enter will propagate by means of a smaller set of connections and thus requires much less computational assets.

Pruning strategies may be utilized at completely different levels of the community’s coaching course of: submit, throughout, or earlier than coaching (i.e., instantly after weight initialization). On this submit, we concentrate on the post-training setting: given a pre-trained community, how can we decide which weights must be pruned? One well-liked technique is magnitude pruning, which removes weights with the smallest magnitude. Whereas environment friendly, this technique doesn’t immediately contemplate the impact of eradicating weights on the community’s efficiency. One other well-liked paradigm is optimization-based pruning, which removes weights primarily based on how a lot their elimination impacts the loss operate. Though conceptually interesting, most current optimization-based approaches appear to face a severe tradeoff between efficiency and computational necessities. Strategies that make crude approximations (e.g., assuming a diagonal Hessian matrix) can scale nicely, however have comparatively low efficiency. However, whereas strategies that make fewer approximations are likely to carry out higher, they seem like a lot much less scalable.

In “Fast as CHITA: Neural Network Pruning with Combinatorial Optimization”, introduced at ICML 2023, we describe how we developed an optimization-based strategy for pruning pre-trained neural networks at scale. CHITA (which stands for “Combinatorial Hessian-free Iterative Thresholding Algorithm”) outperforms current pruning strategies by way of scalability and efficiency tradeoffs, and it does so by leveraging advances from a number of fields, together with high-dimensional statistics, combinatorial optimization, and neural community pruning. For instance, CHITA may be 20x to 1000x quicker than state-of-the-art strategies for pruning ResNet and improves accuracy by over 10% in lots of settings.

Overview of contributions

CHITA has two notable technical enhancements over well-liked strategies:

• Environment friendly use of second-order data: Pruning strategies that use second-order data (i.e., referring to second derivatives) obtain the state-of-the-art in lots of settings. Within the literature, this data is often utilized by computing the Hessian matrix or its inverse, an operation that could be very troublesome to scale as a result of the Hessian dimension is quadratic with respect to the variety of weights. Via cautious reformulation, CHITA makes use of second-order data with out having to compute or retailer the Hessian matrix explicitly, thus permitting for extra scalability.

• Combinatorial optimization: Fashionable optimization-based strategies use a easy optimization method that prunes weights in isolation, i.e., when deciding to prune a sure weight they don’t take into consideration whether or not different weights have been pruned. This might result in pruning essential weights as a result of weights deemed unimportant in isolation might change into essential when different weights are pruned. CHITA avoids this problem through the use of a extra superior, combinatorial optimization algorithm that takes under consideration how pruning one weight impacts others.

Within the sections under, we talk about CHITA’s pruning formulation and algorithms.

A computation-friendly pruning formulation

There are numerous potential pruning candidates, that are obtained by retaining solely a subset of the weights from the unique community. Let ok be a user-specified parameter that denotes the variety of weights to retain. Pruning may be naturally formulated as a best-subset selection (BSS) drawback: amongst all potential pruning candidates (i.e., subsets of weights) with solely ok weights retained, the candidate that has the smallest loss is chosen.

Fixing the pruning BSS drawback on the unique loss operate is usually computationally intractable. Thus, just like earlier work, corresponding to OBD and OBS, we approximate the loss with a quadratic function through the use of a second-order Taylor series, the place the Hessian is estimated with the empirical Fisher information matrix. Whereas gradients may be usually computed effectively, computing and storing the Hessian matrix is prohibitively costly as a consequence of its sheer dimension. Within the literature, it is not uncommon to cope with this problem by making restrictive assumptions on the Hessian (e.g., diagonal matrix) and in addition on the algorithm (e.g., pruning weights in isolation).

CHITA makes use of an environment friendly reformulation of the pruning drawback (BSS utilizing the quadratic loss) that avoids explicitly computing the Hessian matrix, whereas nonetheless utilizing all the knowledge from this matrix. That is made potential by exploiting the low-rank construction of the empirical Fisher data matrix. This reformulation may be seen as a sparse linear regression drawback, the place every regression coefficient corresponds to a sure weight within the neural community. After acquiring an answer to this regression drawback, coefficients set to zero will correspond to weights that must be pruned. Our regression information matrix is (n x p), the place n is the batch (sub-sample) dimension and p is the variety of weights within the authentic community. Usually n << p, so storing and working with this information matrix is way more scalable than frequent pruning approaches that function with the (p x p) Hessian.

Scalable optimization algorithms

CHITA reduces pruning to a linear regression drawback beneath the next sparsity constraint: at most ok regression coefficients may be nonzero. To acquire an answer to this drawback, we contemplate a modification of the well-known iterative hard thresholding (IHT) algorithm. IHT performs gradient descent the place after every replace the next post-processing step is carried out: all regression coefficients exterior the Prime-ok (i.e., the ok coefficients with the most important magnitude) are set to zero. IHT usually delivers an excellent resolution to the issue, and it does so iteratively exploring completely different pruning candidates and collectively optimizing over the weights.

Because of the scale of the issue, commonplace IHT with fixed learning rate can endure from very gradual convergence. For quicker convergence, we developed a brand new line-search technique that exploits the issue construction to discover a appropriate studying price, i.e., one which results in a sufficiently massive lower within the loss. We additionally employed a number of computational schemes to enhance CHITA’s effectivity and the standard of the second-order approximation, resulting in an improved model that we name CHITA++.

Experiments

We evaluate CHITA’s run time and accuracy with a number of state-of-the-art pruning strategies utilizing completely different architectures, together with ResNet and MobileNet.

Run time: CHITA is way more scalable than comparable strategies that carry out joint optimization (versus pruning weights in isolation). For instance, CHITA’s speed-up can attain over 1000x when pruning ResNet.

Publish-pruning accuracy: Beneath, we evaluate the efficiency of CHITA and CHITA++ with magnitude pruning (MP), Woodfisher (WF), and Combinatorial Brain Surgeon (CBS), for pruning 70% of the mannequin weights. Total, we see good enhancements from CHITA and CHITA++.

Subsequent, we report outcomes for pruning a bigger community: ResNet50 (on this community, among the strategies listed within the ResNet20 determine couldn’t scale). Right here we evaluate with magnitude pruning and M-FAC. The determine under exhibits that CHITA achieves higher take a look at accuracy for a variety of sparsity ranges.

Conclusion, limitations, and future work

We introduced CHITA, an optimization-based strategy for pruning pre-trained neural networks. CHITA affords scalability and aggressive efficiency by effectively utilizing second-order data and drawing on concepts from combinatorial optimization and high-dimensional statistics.

CHITA is designed for unstructured pruning wherein any weight may be eliminated. In concept, unstructured pruning can considerably scale back computational necessities. Nonetheless, realizing these reductions in follow requires particular software program (and presumably {hardware}) that assist sparse computations. In distinction, structured pruning, which removes entire buildings like neurons, might supply enhancements which might be simpler to realize on general-purpose software program and {hardware}. It will be fascinating to increase CHITA to structured pruning.

Acknowledgements

This work is a part of a analysis collaboration between Google and MIT. Because of Rahul Mazumder, Natalia Ponomareva, Wenyu Chen, Xiang Meng, Zhe Zhao, and Sergei Vassilvitskii for his or her assist in getting ready this submit and the paper. Additionally due to John Guilyard for creating the graphics on this submit.

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